Build Production Document RAG Pipelines with Pixeltable

The Document Processing Challenge#

Building a document RAG (Retrieval-Augmented Generation) system sounds simple: extract text from PDFs, chunk it, embed it, and search. But production reality is messier:

• PDFs have tables, images, headers, and footers that need special handling
• Documents get updated and need re-processing
• You need to track which chunks came from which documents
• Embeddings need to stay in sync when documents change
• The pipeline needs to scale to thousands of documents

Pixeltable solves all of this with a declarative approach that handles extraction, chunking, embedding, and search in a single unified system.

Basic Document Pipeline#

Let's start with a simple but complete document processing pipeline:

Advanced: Handling Complex PDFs#

Real PDFs have tables, images, and complex layouts. Here's how to handle them:

Smart Chunking Strategies#

Different documents need different chunking strategies:

Enriching Chunks with Metadata#

Add useful metadata to each chunk for better retrieval:

Complete RAG Pipeline with LLM#

Here's a full RAG system that retrieves relevant chunks and generates answers:

Handling Document Updates#

When documents change, Pixeltable's incremental processing ensures only affected chunks are recomputed:

Scaling to Large Document Libraries#

Tips for handling thousands of documents:

Why Pixeltable vs Traditional RAG Stacks#

Task	Traditional Stack	Pixeltable
Text extraction	PyPDF2, pdfplumber, unstructured	Built-in extract_text()
Chunking	LangChain, custom code	Built-in chunks() iterator
Embeddings	Separate embedding service	Computed column with caching
Vector store	Pinecone, Weaviate, pgvector	Built-in embedding index
Orchestration	Airflow, Prefect, custom	Automatic with dependencies
Updates	Manual re-indexing	Automatic incremental updates
Lineage	External tracking	Built-in versioning

Conclusion#

Building production document RAG systems doesn't have to be complex. With Pixeltable's declarative approach:

• Define your pipeline as computed columns
• Let Pixeltable handle extraction, chunking, and embedding
• Get automatic incremental updates when documents change
• Scale from prototypes to production without changing code

The best part? Your entire document pipeline (from raw PDFs to searchable embeddings) lives in one place, fully versioned and queryable.

Resources#

• Pixeltable Documentation
• Production RAG: A Data-Centric Approach
• Embedding Management Guide
• Incremental Embedding Indexes
• Pixeltable on GitHub